| [BMN12] | Peter L. Bartlett, Shahar Mendelson, and Joseph Neeman. l1-regularized linear regression: Persistence and oracle inequalities. Probability Theory and Related Fields, 2012. To appear. [ bib ] |
| [RBHT12] | Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft. Learning in a large function space: Privacy preserving mechanisms for svm learning. Journal of Privacy and Confidentiality, 2012. To appear. [ bib ] |
| [ABRW12] | Alekh Agarwal, Peter Bartlett, Pradeep Ravikumar, and Martin Wainwright. Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. IEEE Transactions on Information Theory, 2012. To appear. [ bib ] |
| [BRS+12] | A. Barth, Benjamin I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, Dawn Song, and Peter L. Bartlett. A learning-based approach to reactive security. IEEE Transactions on Dependable and Secure Computing, 2012. To appear. [ bib ] |
| [AB11] | Sylvain Arlot and Peter L. Bartlett. Margin-adaptive model selection in statistical learning. Bernoulli, 2011. To appear (accepted May 21, 2010). [ bib | .pdf ] |
| [BMP10] | Peter L. Bartlett, Shahar Mendelson, and Petra Philips. On the optimality of sample-based estimates of the expectation of the empirical minimizer. ESAIM: Probability and Statistics, 14:315-337, 2010. [ bib | .pdf | Abstract ] |
| [Bar10] | Peter L. Bartlett. Learning to act in uncertain environments. Communications of the ACM, 53(5):98, 2010. [ bib ] |
| [RBR10] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Corrigendum to `shifting: One-inclusion mistake bounds and sample compression' [j. comput. system sci 75 (1) (2009) 37-59]. Journal of Computer and System Sciences, 76(3-4):278-280, 2010. [ bib ] |
| [RSBN09] | David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, and Partha Niyogi. Multiview point cloud kernels for semisupervised learning. IEEE Signal Processing Magazine, 26(5):145-150, September 2009. [ bib ] |
| [RBR09] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Shifting: one-inclusion mistake bounds and sample compression. Journal of Computer and System Sciences, 75(1):37-59, January 2009. (Was University of California, Berkeley, EECS Department Technical Report EECS-2007-86). [ bib | .pdf ] |
| [Bar08] | Peter L. Bartlett. Fast rates for estimation error and oracle inequalities for model selection. Econometric Theory, 24(2):545-552, 2008. (Was Department of Statistics, U.C. Berkeley Technical Report number 729, 2007). [ bib | .pdf | Abstract ] |
| [BW08] | Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9:1823-1840, 2008. [ bib | .pdf | Abstract ] |
| [CGK+08] | Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, and Peter L. Bartlett. Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. Journal of Machine Learning Research, 9:1775-1822, 2008. [ bib | .pdf | Abstract ] |
| [LBW08] | Wee Sun Lee, Peter L. Bartlett, and Robert C. Williamson. Correction to the importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 54(9):4395, 2008. [ bib | .pdf ] |
| [TB07] | Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8:1007-1025, May 2007. (Invited paper). [ bib | .html ] |
| [BT07a] | Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8:775-790, April 2007. [ bib | .html ] |
| [BT07b] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Journal of Machine Learning Research, 8:2347-2368, 2007. [ bib | .pdf | Abstract ] |
| [BJM06b] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006. (Was Department of Statistics, U.C. Berkeley Technical Report number 638, 2003). [ bib | .ps.gz | .pdf | Abstract ] |
| [BM06b] | Peter L. Bartlett and Shahar Mendelson. Empirical minimization. Probability Theory and Related Fields, 135(3):311-334, 2006. [ bib | .ps.gz | .pdf | Abstract ] |
| [BJM06a] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Comment. Statistical Science, 21(3):341-346, 2006. [ bib ] |
| [BM06a] | Peter L. Bartlett and Shahar Mendelson. Discussion of “2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization” by V. Koltchinskii. The Annals of Statistics, 34(6):2657-2663, 2006. [ bib ] |
| [BBM05] | Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson. Local Rademacher complexities. Annals of Statistics, 33(4):1497-1537, 2005. [ bib | .ps | .pdf | Abstract ] |
| [LCB+04] | G. Lanckriet, N. Cristianini, P. L. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5:27-72, 2004. [ bib | .ps.gz | .pdf ] |
| [GBB04] | E. Greensmith, P. L. Bartlett, and J. Baxter. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5:1471-1530, 2004. [ bib | .pdf ] |
| [BJM04] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Discussion of boosting papers. The Annals of Statistics, 32(1):85-91, 2004. [ bib | .ps.Z | .pdf ] |
| [BM03] | Peter L. Bartlett and Wolfgang Maass. Vapnik-Chervonenkis dimension of neural nets. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1188-1192. MIT Press, 2003. Second Edition. [ bib | .ps.gz | .pdf ] |
| [Bar03] | Peter L. Bartlett. An introduction to reinforcement learning theory: value function methods. In Shahar Mendelson and Alexander J. Smola, editors, Advanced Lectures on Machine Learning, volume 2600, pages 184-202. Springer, 2003. [ bib ] |
| [GBSTW02] | Y. Guo, P. L. Bartlett, J. Shawe-Taylor, and R. C. Williamson. Covering numbers for support vector machines. IEEE Transactions on Information Theory, 48(1):239-250, 2002. [ bib ] |
| [BM02] | P. L. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002. [ bib | .pdf ] |
| [BBL02] | P. L. Bartlett, S. Boucheron, and G. Lugosi. Model selection and error estimation. Machine Learning, 48:85-113, 2002. [ bib | .ps.gz ] |
| [BB02] | P. L. Bartlett and J. Baxter. Estimation and approximation bounds for gradient-based reinforcement learning. Journal of Computer and System Sciences, 64(1):133-150, 2002. [ bib ] |
| [BBD02] | P. L. Bartlett and S. Ben-David. Hardness results for neural network approximation problems. Theoretical Computer Science, 284(1):53-66, 2002. (special issue on Eurocolt'99). [ bib | http ] |
| [BFH02] | P. L. Bartlett, P. Fischer, and K.-U. Höffgen. Exploiting random walks for learning. Information and Computation, 176(2):121-135, 2002. [ bib | http ] |
| [MBG02] | L. Mason, P. L. Bartlett, and M. Golea. Generalization error of combined classifiers. Journal of Computer and System Sciences, 65(2):415-438, 2002. [ bib | http ] |
| [BB01] | J. Baxter and P. L. Bartlett. Infinite-horizon gradient-based policy search. Journal of Artificial Intelligence Research, 15:319-350, 2001. [ bib | .html ] |
| [BBW01] | J. Baxter, P. L. Bartlett, and L. Weaver. Experiments with infinite-horizon, policy-gradient estimation. Journal of Artificial Intelligence Research, 15:351-381, 2001. [ bib | .html ] |
| [AB00] | M. Anthony and P. L. Bartlett. Function learning from interpolation. Combinatorics, Probability, and Computing, 9:213-225, 2000. [ bib ] |
| [MBBF00] | L. Mason, J. Baxter, P. L. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 221-246. MIT Press, 2000. [ bib ] |
| [SBSS00] | A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans. Introduction to large margin classifiers. In Advances in Large Margin Classifiers, pages 1-29. MIT Press, 2000. [ bib ] |
| [BBDK00] | P. L. Bartlett, S. Ben-David, and S. R. Kulkarni. Learning changing concepts by exploiting the structure of change. Machine Learning, 41(2):153-174, 2000. [ bib ] |
| [PPB00] | S. Parameswaran, M. F. Parkinson, and P. L. Bartlett. Profiling in the ASP codesign environment. Journal of Systems Architecture, 46(14):1263-1274, 2000. [ bib ] |
| [SSWB00] | B. Schölkopf, A. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207-1245, 2000. [ bib ] |
| [KBB00] | L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Direct iterative tuning via spectral analysis. Automatica, 36(9):1301-1307, 2000. [ bib ] |
| [MBB00] | L. Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Machine Learning, 38(3):243-255, 2000. [ bib ] |
| [BL99] | P. L. Bartlett and G. Lugosi. An inequality for uniform deviations of sample averages from their means. Statistics and Probability Letters, 44(1):55-62, 1999. [ bib ] |
| [Bar99] | P. L. Bartlett. Efficient neural network learning. In V. D. Blondel, E. D. Sontag, M. Vidyasagar, and J. C. Willems, editors, Open Problems in Mathematical Systems Theory and Control, pages 35-38. Springer Verlag, 1999. [ bib ] |
| [BST99] | P. L. Bartlett and J. Shawe-Taylor. Generalization performance of support vector machines and other pattern classifiers. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 43-54. MIT Press, 1999. [ bib ] |
| [SFBL98] | R. E. Schapire, Y. Freund, P. L. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5):1651-1686, 1998. [ bib ] |
| [BMM98] | P. L. Bartlett, V. Maiorov, and R. Meir. Almost linear VC dimension bounds for piecewise polynomial networks. Neural Computation, 10(8):2159-2173, 1998. [ bib ] |
| [LBW98] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. The importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 44(5):1974-1980, 1998. [ bib ] |
| [STBWA98] | J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926-1940, 1998. [ bib ] |
| [BLL98] | P. L. Bartlett, T. Linder, and G. Lugosi. The minimax distortion redundancy in empirical quantizer design. IEEE Transactions on Information Theory, 44(5):1802-1813, 1998. [ bib ] |
| [BK98] | P. L. Bartlett and S. Kulkarni. The complexity of model classes, and smoothing noisy data. Systems and Control Letters, 34(3):133-140, 1998. [ bib ] |
| [BV98] | P. L. Bartlett and M. Vidyasagar. Introduction to the special issue on learning theory. Systems and Control Letters, 34:113-114, 1998. [ bib ] |
| [KBB98] | L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Optimal controller properties from closed-loop experiments. Automatica, 34(1):83-91, 1998. [ bib ] |
| [BL98] | P. L. Bartlett and P. M. Long. Prediction, learning, uniform convergence, and scale-sensitive dimensions. Journal of Computer and System Sciences, 56(2):174-190, 1998. (special issue on COLT`95). [ bib ] |
| [Bar98] | P. L. Bartlett. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2):525-536, 1998. [ bib ] |
| [BKP97] | P. L. Bartlett, S. R. Kulkarni, and S. E. Posner. Covering numbers for real-valued function classes. IEEE Transactions on Information Theory, 43(5):1721-1724, 1997. [ bib ] |
| [Bar97] | P. L. Bartlett. Book review: `Neural networks for pattern recognition,' Christopher M. Bishop. Statistics in Medicine, 16(20):2385-2386, 1997. [ bib ] |
| [LBW97] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Correction to `lower bounds on the VC-dimension of smoothly parametrized function classes'. Neural Computation, 9:765-769, 1997. [ bib ] |
| [LBW96] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory, 42(6):2118-2132, 1996. [ bib ] |
| [ABIST96] | M. Anthony, P. L. Bartlett, Y. Ishai, and J. Shawe-Taylor. Valid generalisation from approximate interpolation. Combinatorics, Probability, and Computing, 5:191-214, 1996. [ bib ] |
| [BLW96] | P. L. Bartlett, P. M. Long, and R. C. Williamson. Fat-shattering and the learnability of real-valued functions. Journal of Computer and System Sciences, 52(3):434-452, 1996. (special issue on COLT`94). [ bib ] |
| [BW96] | P. L. Bartlett and R. C. Williamson. The Vapnik-Chervonenkis dimension and pseudodimension of two-layer neural networks with discrete inputs. Neural Computation, 8:653-656, 1996. [ bib ] |
| [LBW95] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Lower bounds on the VC-dimension of smoothly parametrized function classes. Neural Computation, 7:990-1002, 1995. (See also correction, Neural Computation, 9: 765-769, 1997). [ bib ] |
| [Bar94] | P. L. Bartlett. Computational learning theory. In A. Kent and J. G. Williams, editors, Encyclopedia of Computer Science and Technology, volume 31, pages 83-99. Marcel Dekker, 1994. [ bib ] |
| [Bar93] | P. L. Bartlett. Vapnik-Chervonenkis dimension bounds for two- and three-layer networks. Neural Computation, 5(3):371-373, 1993. [ bib ] |
| [LBD92] | D. R. Lovell, P. L. Bartlett, and T. Downs. Error and variance bounds on sigmoidal neurons with weight and input errors. Electronics Letters, 28(8):760-762, 1992. [ bib ] |
| [BD92] | P. L. Bartlett and T. Downs. Using random weights to train multi-layer networks of hard-limiting units. IEEE Transactions on Neural Networks, 3(2):202-210, 1992. [ bib ] |
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